Comparison of the effectiveness of mindfulness and homeopathy on sleep and chronic pain in patients with diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) can affect many aspects of life. Numerous psychological interventions have been applied to control this disease. The aim of this study is to examine the effectiveness of mindfulness and homeopathy on sleep and chronic pain in patients with DM in Tehran, Iran. METHODS: The method of study was quasi-experimental with a control group with pretest and posttest stages. The statistical population of the study consisted of all patients with DM referred to the Iranian Diabetes Society (IDS) in Tehran in 2016. Patients with DM with sleep and chronic pain disorders were selected for this study. Among the individuals, 45 people were randomly selected and placed in 3 groups (homeopathy n = 15), mindfulness (n = 15), and control group (n = 15). The two experimental groups (homeopathy and mindfulness) underwent the interventions of homeopathy and mindfulness, respectively, while the control group was in the wait list. All three groups filled out the Pittsburgh Sleep Quality Index (PSQI) and McGill Pain Questionnaire (MPQ) at the baseline and follow-up steps. The data were analyzed using analysis of covariance (ANCOVA) in SPSS software (version 22, IBM Corporation, Armonk, NY, USA). RESULTS: The results showed that mindfulness-based stress reduction (MBRS) and homeopathy could improve chronic pain and sleep disorders in the experimental groups (P < 0.05), however the mindfulness-based treatment was better than homeopathy for reduction of chronic pain and improvement of sleep disorders (P < 0.05). CONCLUSION: Mindfulness and homeopathy are effective in the reduction of chronic pain and sleep disorders in patients with DM, with mindfulness being more effective than homeopathy. This alternative medicine could improve pain and sleep effectively in patients with DM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".